def new_epoch(self, epoch):
# We sample as many architectures as we need
if epoch < self.population_size:
logger.info("Start sampling architectures to fill the population")
# If there is no scope defined, let's use the search space default one
model = torch.nn.Module(
) # hacky way to get arch and accuracy checkpointable
model.arch = sample_random_architecture(self.search_space,
self.scope)
model.accuracy = model.arch.query(self.performance_metric,
self.dataset)
self.population.append(model)
self._update_history(model)
log_every_n_seconds(
logging.INFO,
"Population size {}".format(len(self.population)))
else:
sample = []
while len(sample) < self.sample_size:
candidate = np.random.choice(list(self.population))
sample.append(candidate)
parent = max(sample, key=lambda x: x.accuracy)
child = torch.nn.Module(
) # hacky way to get arch and accuracy checkpointable
child.arch = mutate(parent.arch)
child.accuracy = child.arch.query(self.performance_metric,
self.dataset)
self.population.append(child)
self._update_history(child)
def search(self, resume_from=""):
"""
Start the architecture search.
Generates a json file with training statistics.
Args:
resume_from (str): Checkpoint file to resume from. If not given then
train from scratch.
"""
logger.info("Start training")
self.optimizer.before_training()
checkpoint_freq = self.config.search.checkpoint_freq
if self.optimizer.using_step_function:
self.scheduler = self.build_search_scheduler(
self.optimizer.op_optimizer, self.config)
start_epoch = self._setup_checkpointers(resume_from,
period=checkpoint_freq,
scheduler=self.scheduler)
else:
start_epoch = self._setup_checkpointers(resume_from,
period=checkpoint_freq)
self.train_queue, self.valid_queue, _ = self.build_search_dataloaders(
self.config)
for e in range(start_epoch, self.epochs):
self.optimizer.new_epoch(e)
start_time = time.time()
if self.optimizer.using_step_function:
for step, (data_train, data_val) in enumerate(
zip(self.train_queue, self.valid_queue)):
data_train = (data_train[0].to(self.device),
data_train[1].to(self.device,
non_blocking=True))
data_val = (data_val[0].to(self.device),
data_val[1].to(self.device, non_blocking=True))
stats = self.optimizer.step(data_train, data_val)
logits_train, logits_val, train_loss, val_loss = stats
self._store_accuracies(logits_train, data_train[1],
'train')
self._store_accuracies(logits_val, data_val[1], 'val')
log_every_n_seconds(
logging.INFO,
"Epoch {}-{}, Train loss: {:.5f}, validation loss: {:.5f}, learning rate: {}"
.format(e, step, train_loss, val_loss,
self.scheduler.get_last_lr()),
n=5)
if torch.cuda.is_available():
log_first_n(logging.INFO,
"cuda consumption\n {}".format(
torch.cuda.memory_summary()),
n=3)
self.train_loss.update(float(train_loss.detach().cpu()))
self.val_loss.update(float(val_loss.detach().cpu()))
self.scheduler.step()
end_time = time.time()
self.errors_dict.train_acc.append(self.train_top1.avg)
self.errors_dict.train_loss.append(self.train_loss.avg)
self.errors_dict.valid_acc.append(self.val_top1.avg)
self.errors_dict.valid_loss.append(self.val_loss.avg)
self.errors_dict.runtime.append(end_time - start_time)
else:
end_time = time.time()
train_acc, train_loss, valid_acc, valid_loss = self.optimizer.train_statistics(
)
self.errors_dict.train_acc.append(train_acc)
self.errors_dict.train_loss.append(train_loss)
self.errors_dict.valid_acc.append(valid_acc)
self.errors_dict.valid_loss.append(valid_loss)
self.errors_dict.runtime.append(end_time - start_time)
self.train_top1.avg = train_acc
self.val_top1.avg = valid_acc
self.periodic_checkpointer.step(e)
anytime_results = self.optimizer.test_statistics()
if anytime_results:
# record anytime performance
self.errors_dict.arch_eval.append(anytime_results)
log_every_n_seconds(logging.INFO,
"Epoch {}, Anytime results: {}".format(
e, anytime_results),
n=5)
self._log_to_json()
self._log_and_reset_accuracies(e)
self.optimizer.after_training()
logger.info("Training finished")
def evaluate(
self,
retrain=True,
search_model="",
resume_from="",
best_arch=None,
):
"""
Evaluate the final architecture as given from the optimizer.
If the search space has an interface to a benchmark then query that.
Otherwise train as defined in the config.
Args:
retrain (bool): Reset the weights from the architecure search
search_model (str): Path to checkpoint file that was created during
search. If not provided, then try to load 'model_final.pth' from search
resume_from (str): Resume retraining from the given checkpoint file.
best_arch: Parsed model you want to directly evaluate and ignore the final model
from the optimizer.
"""
logger.info("Start evaluation")
if not best_arch:
if not search_model:
search_model = os.path.join(self.config.save, "search",
"model_final.pth")
self._setup_checkpointers(
search_model) # required to load the architecture
best_arch = self.optimizer.get_final_architecture()
logger.info("Final architecture:\n" + best_arch.modules_str())
if best_arch.QUERYABLE:
metric = Metric.TEST_ACCURACY
result = best_arch.query(metric=metric,
dataset=self.config.dataset)
logger.info("Queried results ({}): {}".format(metric, result))
else:
best_arch.to(self.device)
if retrain:
logger.info("Starting retraining from scratch")
best_arch.reset_weights(inplace=True)
self.train_queue, self.valid_queue, self.test_queue = self.build_eval_dataloaders(
self.config)
optim = self.build_eval_optimizer(best_arch.parameters(),
self.config)
scheduler = self.build_eval_scheduler(optim, self.config)
start_epoch = self._setup_checkpointers(
resume_from,
search=False,
period=self.config.evaluation.checkpoint_freq,
model=best_arch, # checkpointables start here
optim=optim,
scheduler=scheduler)
grad_clip = self.config.evaluation.grad_clip
loss = torch.nn.CrossEntropyLoss()
best_arch.train()
self.train_top1.reset()
self.train_top5.reset()
self.val_top1.reset()
self.val_top5.reset()
# Enable drop path
best_arch.update_edges(update_func=lambda edge: edge.data.set(
'op', DropPathWrapper(edge.data.op)),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# train from scratch
epochs = self.config.evaluation.epochs
for e in range(start_epoch, epochs):
if torch.cuda.is_available():
log_first_n(logging.INFO,
"cuda consumption\n {}".format(
torch.cuda.memory_summary()),
n=20)
# update drop path probability
drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
best_arch.update_edges(
update_func=lambda edge: edge.data.set(
'drop_path_prob', drop_path_prob),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# Train queue
for i, (input_train,
target_train) in enumerate(self.train_queue):
input_train = input_train.to(self.device)
target_train = target_train.to(self.device,
non_blocking=True)
optim.zero_grad()
logits_train = best_arch(input_train)
train_loss = loss(logits_train, target_train)
if hasattr(best_arch,
'auxilary_logits'): # darts specific stuff
log_first_n(logging.INFO,
"Auxiliary is used",
n=10)
auxiliary_loss = loss(best_arch.auxilary_logits(),
target_train)
train_loss += self.config.evaluation.auxiliary_weight * auxiliary_loss
train_loss.backward()
if grad_clip:
torch.nn.utils.clip_grad_norm_(
best_arch.parameters(), grad_clip)
optim.step()
self._store_accuracies(logits_train, target_train,
'train')
log_every_n_seconds(
logging.INFO,
"Epoch {}-{}, Train loss: {:.5}, learning rate: {}"
.format(e, i, train_loss, scheduler.get_last_lr()),
n=5)
# Validation queue
if self.valid_queue:
for i, (input_valid,
target_valid) in enumerate(self.valid_queue):
input_valid = input_valid.cuda().float()
target_valid = target_valid.cuda().float()
# just log the validation accuracy
with torch.no_grad():
logits_valid = best_arch(input_valid)
self._store_accuracies(logits_valid,
target_valid, 'val')
scheduler.step()
self.periodic_checkpointer.step(e)
self._log_and_reset_accuracies(e)
# Disable drop path
best_arch.update_edges(update_func=lambda edge: edge.data.set(
'op', edge.data.op.get_embedded_ops()),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True)
# measure final test accuracy
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
best_arch.eval()
for i, data_test in enumerate(self.test_queue):
input_test, target_test = data_test
input_test = input_test.to(self.device)
target_test = target_test.to(self.device, non_blocking=True)
n = input_test.size(0)
with torch.no_grad():
logits = best_arch(input_test)
prec1, prec5 = utils.accuracy(logits,
target_test,
topk=(1, 5))
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
log_every_n_seconds(logging.INFO,
"Inference batch {} of {}.".format(
i, len(self.test_queue)),
n=5)
logger.info(
"Evaluation finished. Test accuracies: top-1 = {:.5}, top-5 = {:.5}"
.format(top1.avg, top5.avg))
def evaluate(
self,
retrain=True,
search_model="",
resume_from="",
best_arch=None,
):
"""
Evaluate the final architecture as given from the optimizer.
If the search space has an interface to a benchmark then query that.
Otherwise train as defined in the config.
Args:
retrain (bool): Reset the weights from the architecure search
search_model (str): Path to checkpoint file that was created during
search. If not provided, then try to load 'model_final.pth' from search
resume_from (str): Resume retraining from the given checkpoint file.
multi_gpu (bool): Distribute training on multiple gpus.
best_arch: Parsed model you want to directly evaluate and ignore the final model
from the optimizer.
"""
#best_arch.to(self.device)
self.config.evaluation.resume_from = resume_from
if retrain:
if self.config.gpu is not None:
logger.warning(
'You have chosen a specific GPU. This will completely \
disable data parallelism.'
)
if self.config.evaluation.dist_url == "env://" and self.config.evaluation.world_size == -1:
self.config.evaluation.world_size = int(os.environ["WORLD_SIZE"])
self.config.evaluation.distributed = \
self.config.evaluation.world_size > 1 or self.config.evaluation.multiprocessing_distributed
ngpus_per_node = torch.cuda.device_count()
if self.config.evaluation.multiprocessing_distributed:
# Since we have ngpus_per_node processes per node, the
# total world_size needs to be adjusted
self.config.evaluation.world_size = ngpus_per_node * self.config.evaluation.world_size
# Use torch.multiprocessing.spawn to launch distributed
# processes: the main_worker process function
mp.spawn(self.main_worker, nprocs=ngpus_per_node,
args=(ngpus_per_node, self.config.evaluation,
search_model, best_arch))
else:
# Simply call main_worker function
self.main_worker(self.config.gpu, ngpus_per_node,
self.config.evaluation,
search_model, best_arch)
if not self.QUERYABLE:
# Disable drop path
best_arch.update_edges(
update_func=lambda edge: edge.data.set('op', edge.data.op.get_embedded_ops()),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True
)
# measure final test accuracy
top1 = utils.AverageMeter()
top5 = utils.AverageMeter()
best_arch.eval()
for i, data_test in enumerate(self.test_queue):
input_test, target_test = data_test
input_test = input_test.to(self.device)
target_test = target_test.to(self.device, non_blocking=True)
n = input_test.size(0)
with torch.no_grad():
logits = best_arch(input_test)
prec1, prec5 = utils.accuracy(logits, target_test, topk=(1, 5))
top1.update(prec1.data.item(), n)
top5.update(prec5.data.item(), n)
log_every_n_seconds(
logging.INFO,
"Inference batch {} of {}.".format(
i, len(self.test_queue)
), n=5
)
logger.info("Evaluation finished. Test accuracies: top-1 = {:.5}, \
top-5 = {:.5}".format(top1.avg, top5.avg))
def main_worker(self, gpu, ngpus_per_node, args, search_model, best_arch):
logger.info("Start evaluation")
if not best_arch:
if not search_model:
search_model = os.path.join(self.config.save, "search", "model_final.pth")
self._setup_checkpointers(search_model) # required to load the architecture
best_arch = self.optimizer.get_final_architecture()
logger.info("Final architecture:\n" + best_arch.modules_str())
if best_arch.QUERYABLE:
metric = Metric.TEST_ACCURACY
result = best_arch.query(
metric=metric, dataset=self.config.dataset
)
logger.info("Queried results ({}): {}".format(metric, result))
self.QUERYABLE = True
return
best_arch.reset_weights(inplace=True)
logger.info("Starting retraining from scratch")
args.gpu = gpu
if gpu is not None:
logger.info("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
world_size=args.world_size, rank=args.rank)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow!")
elif args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
best_arch.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) /
ngpus_per_node)
best_arch = \
torch.nn.parallel.DistributedDataParallel(best_arch,
device_ids=[args.gpu])
else:
best_arch.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
best_arch = torch.nn.parallel.DistributedDataParallel(best_arch)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
best_arch = best_arch.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
best_arch = torch.nn.DataParallel(best_arch).cuda()
cudnn.benchmark = True
self.train_queue, self.valid_queue, self.test_queue =\
self.build_eval_dataloaders(self.config)
optim = self.build_eval_optimizer(best_arch.parameters(), self.config)
scheduler = self.build_eval_scheduler(optim, self.config)
start_epoch = self._setup_checkpointers(args.resume_from,
search=False,
period=self.config.evaluation.checkpoint_freq,
model=best_arch, # checkpointables start here
optim=optim,
scheduler=scheduler
)
grad_clip = self.config.evaluation.grad_clip
loss = torch.nn.CrossEntropyLoss()
best_arch.train()
self.train_top1.reset()
self.train_top5.reset()
self.val_top1.reset()
self.val_top5.reset()
# Enable drop path
if isinstance(best_arch, torch.nn.DataParallel):
best_arch.module.update_edges(
update_func=lambda edge: edge.data.set('op', DropPathWrapper(edge.data.op)),
scope=best_arch.module.OPTIMIZER_SCOPE,
private_edge_data=True
)
else:
best_arch.update_edges(
update_func=lambda edge: edge.data.set('op', DropPathWrapper(edge.data.op)),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True
)
# train from scratch
epochs = self.config.evaluation.epochs
for e in range(start_epoch, epochs):
# update drop path probability
drop_path_prob = self.config.evaluation.drop_path_prob * e / epochs
if isinstance(best_arch, torch.nn.DataParallel):
best_arch.module.update_edges(
update_func=lambda edge: edge.data.set('drop_path_prob', drop_path_prob),
scope=best_arch.module.OPTIMIZER_SCOPE,
private_edge_data=True
)
else:
best_arch.update_edges(
update_func=lambda edge: edge.data.set('drop_path_prob', drop_path_prob),
scope=best_arch.OPTIMIZER_SCOPE,
private_edge_data=True
)
# Train queue
for i, (input_train, target_train) in enumerate(self.train_queue):
input_train = input_train.to(self.device)
target_train = target_train.to(self.device, non_blocking=True)
optim.zero_grad()
logits_train = best_arch(input_train)
train_loss = loss(logits_train, target_train)
if hasattr(best_arch, 'auxilary_logits'): # darts specific stuff
log_first_n(logging.INFO, "Auxiliary is used", n=10)
auxiliary_loss = loss(best_arch.auxilary_logits(), target_train)
train_loss += self.config.evaluation.auxiliary_weight * auxiliary_loss
train_loss.backward()
if grad_clip:
torch.nn.utils.clip_grad_norm_(best_arch.parameters(), grad_clip)
optim.step()
self._store_accuracies(logits_train, target_train, 'train')
log_every_n_seconds(
logging.INFO,
"Epoch {}-{}, Train loss: {:.5}, learning rate: {}".format(
e, i, train_loss, scheduler.get_last_lr()
), n=5
)
if torch.cuda.is_available():
log_first_n(
logging.INFO,
"cuda consumption\n {}".format(
torch.cuda.memory_summary()
), n=3
)
# Validation queue
if self.valid_queue:
for i, (input_valid, target_valid) in enumerate(self.valid_queue):
input_valid = input_valid.to(self.device).float()
target_valid = target_valid.to(self.device, non_blocking=True).float()
# just log the validation accuracy
logits_valid = best_arch(input_valid)
self._store_accuracies(logits_valid, target_valid, 'val')
scheduler.step()
self.periodic_checkpointer.step(e)
self._log_and_reset_accuracies(e)
